Abstract:

[Objective] This study addresses regional disparities and imbalanced drivers in developing smart agriculture, a core approach to fostering new quality agricultural productivity. It aims to: (1) construct a comprehensive evaluation system incorporating geographical and industrial heterogeneity; (2) empirically analyze the synergistic drive between data elements and agricultural physical capital; and (3) reveal their role in inter-provincial spatial linkages. The significance lies in its potential to inform strategic planning and policy-making, thereby contributing to the sustainable transformation of agriculture and balanced regional development within the context of rural revitalization. [Methods] A quantitative spatial econometric approach was employed using panel data from 30 Chinese provinces spanning from 2015 to 2023. The research was executed in three key stages. First, a comprehensive provincial-level Smart Agriculture Development Index was constructed. This index integrated multiple dimensions and was weighted by combining the Analytic Hierarchy Process and the Entropy method, with adjustments made for terrain and leading industry heterogeneity. Second, a series of econometric models were specified. Baseline fixed-effects and Generalized [Method] of Moments models were used to examine the driving role of data elements, while mediating and moderating effect models were employed to systematically verify the synergistic mechanism between data and physical capital and its pathways. Third, spatial autocorrelation tests and Spatial Durbin Models were employed with three spatial weight matrices—geographical contiguity, agricultural resource zoning, and agricultural economic structure similarity—to identify spatial correlation characteristics and spillover patterns. Direct and indirect effects were decomposed to precisely quantify local impacts and spatial spillovers. [Results and Discussions] The analysis yielded four clusters of key findings that confirmed and refined our core hypotheses. 1) Gradient development and regional mismatch: Smart agriculture development exhibited a pronounced "ladder-like" spatial pattern. The Huang-Huai-Hai region remained the persistent leader in absolute development level. The southwestern region also maintained a relatively high level, which appeared partly "forced" by its challenging terrain, necessitating efficiency-seeking technology. In contrast, the northeast and northwest regions lagged. A critical systemic mismatch was revealed: regions with the highest growth momentum were not necessarily those with the highest current smartization levels, indicating divergent developmental pathways. 2) The core synergistic mechanism: A significant positive interaction was found between data inputs and agricultural physical capital. Crucially, the independent coefficients of each were often found to be insignificant or even negative in spatial models, which underscored that limited or even negative marginal returns were yielded by isolated, uncoordinated investment in either domain. Significant systemic gains were unlocked precisely by their structural complementarity. Furthermore, this synergy was found to operate partially through the channel of technological capital accumulation. The mediating effect of technological capital was confirmed to be significant, and its own impact on smart agriculture output was exhibited as a nonlinear threshold characteristic. This confirmed that a critical mass of technological capital had to be accumulated before its benefits could be fully realized. 3) Competition-dominated spatial interactions: The spatial analysis revealed that inter-provincial dynamics were characterized primarily by competition rather than cooperation. A significant negative spatial spillover was detected specifically under the economic structure similarity matrix. This indicated that resources, talent, and investment were competed for by provinces with similar agricultural economic profiles, potentially hindering each other's growth. However, a nuanced finding was observed: while raw data or capital might be siphoned away, significant positive spatial spillovers were generated by successful provincial models of "data-capital" synergy. This suggested that best practices and development paradigms could be diffused, offering a pathway to transcend pure competition. 4) The effectiveness of the core drivers was found to be highly context-dependent. From a zoning perspective, the Huang-Huai-Hai region was characterized by digital-drive but was found to lack deep synergy; the northeast was constrained by traditional path dependency; the southwest was shown to exhibit a paradox of high knowledge spillover but low local application; and the northwest was characterized by singular, weak drivers. From a developmental stage perspective, the role of R&D investment was assessed as stable, while the payoff from digital infrastructure was seen to be contingent on an "efficiency threshold", and its spatial spillover effect was observed to diminish as regional Total Factor Productivity increased. [Conclusions] It is demonstrated that development is not merely a function of increased inputs, but is critically determined by the structural coupling of data and physical capital. This coupling is facilitated by technological capital, which acts as a nonlinear mediator. At a practical level, a one-size-fits-all approach to investment policy is argued against. For leading regions such as the Huang-Huai-Hai, policy focus should be placed on deepening existing synergies. For regions like the northeast, breaking path dependence is seen to require policies that forcefully couple new digital tools with legacy physical assets. The pervasive "siphon effect" is identified as necessitating national-level coordination mechanisms among structurally similar provinces to mitigate destructive competition. Ultimately, the promotion of smart agriculture is concluded to require spatially differentiated policies that strategically foster local factor synergy while managing the competitive externalities inherent in regional linkages.

Key words: smart agriculture, technological input, data factor, spatial spillover, Spatial Durbin Model